{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "sapphire-jurisdiction",
   "metadata": {},
   "source": [
    "# Import Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "killing-expansion",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic Imports \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Plotting \n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# Preprocessing\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Metrics \n",
    "from sklearn.metrics import mean_squared_error as  mse, mean_absolute_error as mae\n",
    "\n",
    "# ML Models\n",
    "import lightgbm as lgb\n",
    "from lightgbm import LGBMRegressor \n",
    "import xgboost as xg \n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import svm\n",
    "\n",
    "# Model Tuning \n",
    "from bayes_opt import BayesianOptimization\n",
    "\n",
    "# Feature Importance \n",
    "import shap\n",
    "\n",
    "# Ignore Warnings \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "commercial-superior",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('../data_files/House Price_data/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "invisible-universe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "regulated-failing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460</td>\n",
       "      <td>91</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>281</td>\n",
       "      <td>54</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gd</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1454</td>\n",
       "      <td>50</td>\n",
       "      <td>925</td>\n",
       "      <td>1311</td>\n",
       "      <td>1459</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>157</td>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1267</td>\n",
       "      <td>1198</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>56.897260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.049958</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.610009</td>\n",
       "      <td>42.300571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.284752</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>365.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1095.250000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15500.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Id   MSSubClass MSZoning  LotFrontage        LotArea Street  \\\n",
       "count   1460.000000  1460.000000     1460  1201.000000    1460.000000   1460   \n",
       "unique          NaN          NaN        5          NaN            NaN      2   \n",
       "top             NaN          NaN       RL          NaN            NaN   Pave   \n",
       "freq            NaN          NaN     1151          NaN            NaN   1454   \n",
       "mean     730.500000    56.897260      NaN    70.049958   10516.828082    NaN   \n",
       "std      421.610009    42.300571      NaN    24.284752    9981.264932    NaN   \n",
       "min        1.000000    20.000000      NaN    21.000000    1300.000000    NaN   \n",
       "25%      365.750000    20.000000      NaN    59.000000    7553.500000    NaN   \n",
       "50%      730.500000    50.000000      NaN    69.000000    9478.500000    NaN   \n",
       "75%     1095.250000    70.000000      NaN    80.000000   11601.500000    NaN   \n",
       "max     1460.000000   190.000000      NaN   313.000000  215245.000000    NaN   \n",
       "\n",
       "       Alley LotShape LandContour Utilities  ...     PoolArea PoolQC  Fence  \\\n",
       "count     91     1460        1460      1460  ...  1460.000000      7    281   \n",
       "unique     2        4           4         2  ...          NaN      3      4   \n",
       "top     Grvl      Reg         Lvl    AllPub  ...          NaN     Gd  MnPrv   \n",
       "freq      50      925        1311      1459  ...          NaN      3    157   \n",
       "mean     NaN      NaN         NaN       NaN  ...     2.758904    NaN    NaN   \n",
       "std      NaN      NaN         NaN       NaN  ...    40.177307    NaN    NaN   \n",
       "min      NaN      NaN         NaN       NaN  ...     0.000000    NaN    NaN   \n",
       "25%      NaN      NaN         NaN       NaN  ...     0.000000    NaN    NaN   \n",
       "50%      NaN      NaN         NaN       NaN  ...     0.000000    NaN    NaN   \n",
       "75%      NaN      NaN         NaN       NaN  ...     0.000000    NaN    NaN   \n",
       "max      NaN      NaN         NaN       NaN  ...   738.000000    NaN    NaN   \n",
       "\n",
       "       MiscFeature       MiscVal       MoSold       YrSold  SaleType  \\\n",
       "count           54   1460.000000  1460.000000  1460.000000      1460   \n",
       "unique           4           NaN          NaN          NaN         9   \n",
       "top           Shed           NaN          NaN          NaN        WD   \n",
       "freq            49           NaN          NaN          NaN      1267   \n",
       "mean           NaN     43.489041     6.321918  2007.815753       NaN   \n",
       "std            NaN    496.123024     2.703626     1.328095       NaN   \n",
       "min            NaN      0.000000     1.000000  2006.000000       NaN   \n",
       "25%            NaN      0.000000     5.000000  2007.000000       NaN   \n",
       "50%            NaN      0.000000     6.000000  2008.000000       NaN   \n",
       "75%            NaN      0.000000     8.000000  2009.000000       NaN   \n",
       "max            NaN  15500.000000    12.000000  2010.000000       NaN   \n",
       "\n",
       "        SaleCondition      SalePrice  \n",
       "count            1460    1460.000000  \n",
       "unique              6            NaN  \n",
       "top            Normal            NaN  \n",
       "freq             1198            NaN  \n",
       "mean              NaN  180921.195890  \n",
       "std               NaN   79442.502883  \n",
       "min               NaN   34900.000000  \n",
       "25%               NaN  129975.000000  \n",
       "50%               NaN  163000.000000  \n",
       "75%               NaN  214000.000000  \n",
       "max               NaN  755000.000000  \n",
       "\n",
       "[11 rows x 81 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "original-extension",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "greenhouse-spare",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      "Id               1460 non-null int64\n",
      "MSSubClass       1460 non-null int64\n",
      "MSZoning         1460 non-null object\n",
      "LotFrontage      1201 non-null float64\n",
      "LotArea          1460 non-null int64\n",
      "Street           1460 non-null object\n",
      "Alley            91 non-null object\n",
      "LotShape         1460 non-null object\n",
      "LandContour      1460 non-null object\n",
      "Utilities        1460 non-null object\n",
      "LotConfig        1460 non-null object\n",
      "LandSlope        1460 non-null object\n",
      "Neighborhood     1460 non-null object\n",
      "Condition1       1460 non-null object\n",
      "Condition2       1460 non-null object\n",
      "BldgType         1460 non-null object\n",
      "HouseStyle       1460 non-null object\n",
      "OverallQual      1460 non-null int64\n",
      "OverallCond      1460 non-null int64\n",
      "YearBuilt        1460 non-null int64\n",
      "YearRemodAdd     1460 non-null int64\n",
      "RoofStyle        1460 non-null object\n",
      "RoofMatl         1460 non-null object\n",
      "Exterior1st      1460 non-null object\n",
      "Exterior2nd      1460 non-null object\n",
      "MasVnrType       1452 non-null object\n",
      "MasVnrArea       1452 non-null float64\n",
      "ExterQual        1460 non-null object\n",
      "ExterCond        1460 non-null object\n",
      "Foundation       1460 non-null object\n",
      "BsmtQual         1423 non-null object\n",
      "BsmtCond         1423 non-null object\n",
      "BsmtExposure     1422 non-null object\n",
      "BsmtFinType1     1423 non-null object\n",
      "BsmtFinSF1       1460 non-null int64\n",
      "BsmtFinType2     1422 non-null object\n",
      "BsmtFinSF2       1460 non-null int64\n",
      "BsmtUnfSF        1460 non-null int64\n",
      "TotalBsmtSF      1460 non-null int64\n",
      "Heating          1460 non-null object\n",
      "HeatingQC        1460 non-null object\n",
      "CentralAir       1460 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1460 non-null int64\n",
      "2ndFlrSF         1460 non-null int64\n",
      "LowQualFinSF     1460 non-null int64\n",
      "GrLivArea        1460 non-null int64\n",
      "BsmtFullBath     1460 non-null int64\n",
      "BsmtHalfBath     1460 non-null int64\n",
      "FullBath         1460 non-null int64\n",
      "HalfBath         1460 non-null int64\n",
      "BedroomAbvGr     1460 non-null int64\n",
      "KitchenAbvGr     1460 non-null int64\n",
      "KitchenQual      1460 non-null object\n",
      "TotRmsAbvGrd     1460 non-null int64\n",
      "Functional       1460 non-null object\n",
      "Fireplaces       1460 non-null int64\n",
      "FireplaceQu      770 non-null object\n",
      "GarageType       1379 non-null object\n",
      "GarageYrBlt      1379 non-null float64\n",
      "GarageFinish     1379 non-null object\n",
      "GarageCars       1460 non-null int64\n",
      "GarageArea       1460 non-null int64\n",
      "GarageQual       1379 non-null object\n",
      "GarageCond       1379 non-null object\n",
      "PavedDrive       1460 non-null object\n",
      "WoodDeckSF       1460 non-null int64\n",
      "OpenPorchSF      1460 non-null int64\n",
      "EnclosedPorch    1460 non-null int64\n",
      "3SsnPorch        1460 non-null int64\n",
      "ScreenPorch      1460 non-null int64\n",
      "PoolArea         1460 non-null int64\n",
      "PoolQC           7 non-null object\n",
      "Fence            281 non-null object\n",
      "MiscFeature      54 non-null object\n",
      "MiscVal          1460 non-null int64\n",
      "MoSold           1460 non-null int64\n",
      "YrSold           1460 non-null int64\n",
      "SaleType         1460 non-null object\n",
      "SaleCondition    1460 non-null object\n",
      "SalePrice        1460 non-null int64\n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info(verbose = True,null_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "experienced-montana",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "sacred-pledge",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature      Number of NaN\n",
      "PoolQC          1453\n",
      "MiscFeature     1406\n",
      "Alley           1369\n",
      "Fence           1179\n",
      "FireplaceQu      690\n",
      "LotFrontage      259\n",
      "GarageCond        81\n",
      "GarageType        81\n",
      "GarageYrBlt       81\n",
      "GarageFinish      81\n",
      "GarageQual        81\n",
      "BsmtExposure      38\n",
      "BsmtFinType2      38\n",
      "BsmtFinType1      37\n",
      "BsmtCond          37\n",
      "BsmtQual          37\n",
      "MasVnrArea         8\n",
      "MasVnrType         8\n",
      "Electrical         1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Feature      Number of NaN')\n",
    "print(train.isnull().sum().sort_values(ascending=False).loc[lambda x : x!=0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "continental-guide",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('../data_files/House Price_data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "pointed-europe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1459 entries, 0 to 1458\n",
      "Data columns (total 80 columns):\n",
      "Id               1459 non-null int64\n",
      "MSSubClass       1459 non-null int64\n",
      "MSZoning         1455 non-null object\n",
      "LotFrontage      1232 non-null float64\n",
      "LotArea          1459 non-null int64\n",
      "Street           1459 non-null object\n",
      "Alley            107 non-null object\n",
      "LotShape         1459 non-null object\n",
      "LandContour      1459 non-null object\n",
      "Utilities        1457 non-null object\n",
      "LotConfig        1459 non-null object\n",
      "LandSlope        1459 non-null object\n",
      "Neighborhood     1459 non-null object\n",
      "Condition1       1459 non-null object\n",
      "Condition2       1459 non-null object\n",
      "BldgType         1459 non-null object\n",
      "HouseStyle       1459 non-null object\n",
      "OverallQual      1459 non-null int64\n",
      "OverallCond      1459 non-null int64\n",
      "YearBuilt        1459 non-null int64\n",
      "YearRemodAdd     1459 non-null int64\n",
      "RoofStyle        1459 non-null object\n",
      "RoofMatl         1459 non-null object\n",
      "Exterior1st      1458 non-null object\n",
      "Exterior2nd      1458 non-null object\n",
      "MasVnrType       1443 non-null object\n",
      "MasVnrArea       1444 non-null float64\n",
      "ExterQual        1459 non-null object\n",
      "ExterCond        1459 non-null object\n",
      "Foundation       1459 non-null object\n",
      "BsmtQual         1415 non-null object\n",
      "BsmtCond         1414 non-null object\n",
      "BsmtExposure     1415 non-null object\n",
      "BsmtFinType1     1417 non-null object\n",
      "BsmtFinSF1       1458 non-null float64\n",
      "BsmtFinType2     1417 non-null object\n",
      "BsmtFinSF2       1458 non-null float64\n",
      "BsmtUnfSF        1458 non-null float64\n",
      "TotalBsmtSF      1458 non-null float64\n",
      "Heating          1459 non-null object\n",
      "HeatingQC        1459 non-null object\n",
      "CentralAir       1459 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1459 non-null int64\n",
      "2ndFlrSF         1459 non-null int64\n",
      "LowQualFinSF     1459 non-null int64\n",
      "GrLivArea        1459 non-null int64\n",
      "BsmtFullBath     1457 non-null float64\n",
      "BsmtHalfBath     1457 non-null float64\n",
      "FullBath         1459 non-null int64\n",
      "HalfBath         1459 non-null int64\n",
      "BedroomAbvGr     1459 non-null int64\n",
      "KitchenAbvGr     1459 non-null int64\n",
      "KitchenQual      1458 non-null object\n",
      "TotRmsAbvGrd     1459 non-null int64\n",
      "Functional       1457 non-null object\n",
      "Fireplaces       1459 non-null int64\n",
      "FireplaceQu      729 non-null object\n",
      "GarageType       1383 non-null object\n",
      "GarageYrBlt      1381 non-null float64\n",
      "GarageFinish     1381 non-null object\n",
      "GarageCars       1458 non-null float64\n",
      "GarageArea       1458 non-null float64\n",
      "GarageQual       1381 non-null object\n",
      "GarageCond       1381 non-null object\n",
      "PavedDrive       1459 non-null object\n",
      "WoodDeckSF       1459 non-null int64\n",
      "OpenPorchSF      1459 non-null int64\n",
      "EnclosedPorch    1459 non-null int64\n",
      "3SsnPorch        1459 non-null int64\n",
      "ScreenPorch      1459 non-null int64\n",
      "PoolArea         1459 non-null int64\n",
      "PoolQC           3 non-null object\n",
      "Fence            290 non-null object\n",
      "MiscFeature      51 non-null object\n",
      "MiscVal          1459 non-null int64\n",
      "MoSold           1459 non-null int64\n",
      "YrSold           1459 non-null int64\n",
      "SaleType         1458 non-null object\n",
      "SaleCondition    1459 non-null object\n",
      "dtypes: float64(11), int64(26), object(43)\n",
      "memory usage: 912.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "executive-samoa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature      Number of NaN\n",
      "PoolQC          1456\n",
      "MiscFeature     1408\n",
      "Alley           1352\n",
      "Fence           1169\n",
      "FireplaceQu      730\n",
      "LotFrontage      227\n",
      "GarageCond        78\n",
      "GarageQual        78\n",
      "GarageYrBlt       78\n",
      "GarageFinish      78\n",
      "GarageType        76\n",
      "BsmtCond          45\n",
      "BsmtQual          44\n",
      "BsmtExposure      44\n",
      "BsmtFinType1      42\n",
      "BsmtFinType2      42\n",
      "MasVnrType        16\n",
      "MasVnrArea        15\n",
      "MSZoning           4\n",
      "BsmtHalfBath       2\n",
      "Utilities          2\n",
      "Functional         2\n",
      "BsmtFullBath       2\n",
      "BsmtFinSF2         1\n",
      "BsmtFinSF1         1\n",
      "Exterior2nd        1\n",
      "BsmtUnfSF          1\n",
      "TotalBsmtSF        1\n",
      "SaleType           1\n",
      "Exterior1st        1\n",
      "KitchenQual        1\n",
      "GarageArea         1\n",
      "GarageCars         1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Feature      Number of NaN')\n",
    "print(test.isnull().sum().sort_values(ascending=False).loc[lambda x : x!=0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "behind-reporter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature      Number of NaN\n",
      "PoolQC         1456\n",
      "MiscFeature    1408\n",
      "Alley          1352\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "drop_columns = (train.isnull().sum().sort_values(ascending=False).loc[lambda x : x> .90*1460]).index.to_list()\n",
    "drop_columns.append('Id')\n",
    "\n",
    "train_Id = train['Id'].to_list()\n",
    "test_Id = test['Id'].to_list()\n",
    "\n",
    "print('Feature      Number of NaN')\n",
    "print(test.isnull().sum().sort_values(ascending=False).loc[lambda x : x>.90*1460])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "technological-cloud",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_clean\n",
      "Feature      Number of NaN\n",
      "Fence          1179\n",
      "FireplaceQu     690\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "test_clean\n",
      "Feature      Number of NaN\n",
      "Fence          1169\n",
      "FireplaceQu     730\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "train_clean = train.drop(drop_columns,axis=1,errors='ignore')\n",
    "test_clean = test.drop(drop_columns,axis=1,errors='ignore')\n",
    "\n",
    "train_10_percent_missing_features = train_clean.isnull().sum().sort_values(ascending=False).loc[lambda x :(x < .10 * 1460)].index.to_list()\n",
    "# 离散型\n",
    "train_10_percent_missing_features_cat = train_clean[train_10_percent_missing_features].select_dtypes('object').columns.to_list()\n",
    "# 连续型\n",
    "train_10_percent_missing_features_num = train_clean[train_10_percent_missing_features].select_dtypes('number').columns.to_list()\n",
    "\n",
    "# 众数填充\n",
    "train_clean[train_10_percent_missing_features_cat] = train_clean[train_10_percent_missing_features_cat].fillna(train_clean[train_10_percent_missing_features_cat].mode().iloc[0])\n",
    "# 中位数填充\n",
    "train_clean[train_10_percent_missing_features_num] = train_clean[train_10_percent_missing_features_num].fillna(train_clean[train_10_percent_missing_features_num].median().iloc[0])\n",
    "\n",
    "test_10_percent_missing_features = test_clean.isnull().sum().sort_values(ascending=False).loc[lambda x : (x<.10*1460)  & (x != 0)].index.to_list()\n",
    "test_10_percent_missing_features_cat = test_clean[test_10_percent_missing_features].select_dtypes('object').columns.to_list()\n",
    "test_10_percent_missing_features_num = test_clean[test_10_percent_missing_features].select_dtypes('number').columns.to_list()\n",
    "\n",
    "test_clean[test_10_percent_missing_features_cat] = test_clean[test_10_percent_missing_features_cat].fillna(test_clean[test_10_percent_missing_features_cat].mode().iloc[0])\n",
    "test_clean[test_10_percent_missing_features_num] = test_clean[test_10_percent_missing_features_num].fillna(test_clean[test_10_percent_missing_features_num].median().iloc[0])\n",
    "\n",
    "train_clean[\"LotFrontage\"] = train_clean[\"LotFrontage\"].fillna(train_clean[\"LotFrontage\"].median())\n",
    "test_clean[\"LotFrontage\"] = test_clean[\"LotFrontage\"].fillna(test_clean[\"LotFrontage\"].median())\n",
    "\n",
    "print(\"train_clean\")\n",
    "print('Feature      Number of NaN')\n",
    "print(train_clean.isnull().sum().sort_values(ascending=False).loc[lambda x : x!=0])\n",
    "\n",
    "print('\\n')\n",
    "\n",
    "print(\"test_clean\")\n",
    "print('Feature      Number of NaN')\n",
    "print(test_clean.isnull().sum().sort_values(ascending=False).loc[lambda x : x!=0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "diagnostic-ranch",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2919 entries, 0 to 2918\n",
      "Data columns (total 76 columns):\n",
      "MSSubClass       2919 non-null int64\n",
      "MSZoning         2919 non-null object\n",
      "LotFrontage      2919 non-null float64\n",
      "LotArea          2919 non-null int64\n",
      "Street           2919 non-null object\n",
      "LotShape         2919 non-null object\n",
      "LandContour      2919 non-null object\n",
      "Utilities        2919 non-null object\n",
      "LotConfig        2919 non-null object\n",
      "LandSlope        2919 non-null object\n",
      "Neighborhood     2919 non-null object\n",
      "Condition1       2919 non-null object\n",
      "Condition2       2919 non-null object\n",
      "BldgType         2919 non-null object\n",
      "HouseStyle       2919 non-null object\n",
      "OverallQual      2919 non-null int64\n",
      "OverallCond      2919 non-null int64\n",
      "YearBuilt        2919 non-null int64\n",
      "YearRemodAdd     2919 non-null int64\n",
      "RoofStyle        2919 non-null object\n",
      "RoofMatl         2919 non-null object\n",
      "Exterior1st      2919 non-null object\n",
      "Exterior2nd      2919 non-null object\n",
      "MasVnrType       2919 non-null object\n",
      "MasVnrArea       2919 non-null float64\n",
      "ExterQual        2919 non-null object\n",
      "ExterCond        2919 non-null object\n",
      "Foundation       2919 non-null object\n",
      "BsmtQual         2919 non-null object\n",
      "BsmtCond         2919 non-null object\n",
      "BsmtExposure     2919 non-null object\n",
      "BsmtFinType1     2919 non-null object\n",
      "BsmtFinSF1       2919 non-null float64\n",
      "BsmtFinType2     2919 non-null object\n",
      "BsmtFinSF2       2919 non-null float64\n",
      "BsmtUnfSF        2919 non-null float64\n",
      "TotalBsmtSF      2919 non-null float64\n",
      "Heating          2919 non-null object\n",
      "HeatingQC        2919 non-null object\n",
      "CentralAir       2919 non-null object\n",
      "Electrical       2919 non-null object\n",
      "1stFlrSF         2919 non-null int64\n",
      "2ndFlrSF         2919 non-null int64\n",
      "LowQualFinSF     2919 non-null int64\n",
      "GrLivArea        2919 non-null int64\n",
      "BsmtFullBath     2919 non-null float64\n",
      "BsmtHalfBath     2919 non-null float64\n",
      "FullBath         2919 non-null int64\n",
      "HalfBath         2919 non-null int64\n",
      "BedroomAbvGr     2919 non-null int64\n",
      "KitchenAbvGr     2919 non-null int64\n",
      "KitchenQual      2919 non-null object\n",
      "TotRmsAbvGrd     2919 non-null int64\n",
      "Functional       2919 non-null object\n",
      "Fireplaces       2919 non-null int64\n",
      "FireplaceQu      1499 non-null object\n",
      "GarageType       2919 non-null object\n",
      "GarageYrBlt      2919 non-null float64\n",
      "GarageFinish     2919 non-null object\n",
      "GarageCars       2919 non-null float64\n",
      "GarageArea       2919 non-null float64\n",
      "GarageQual       2919 non-null object\n",
      "GarageCond       2919 non-null object\n",
      "PavedDrive       2919 non-null object\n",
      "WoodDeckSF       2919 non-null int64\n",
      "OpenPorchSF      2919 non-null int64\n",
      "EnclosedPorch    2919 non-null int64\n",
      "3SsnPorch        2919 non-null int64\n",
      "ScreenPorch      2919 non-null int64\n",
      "PoolArea         2919 non-null int64\n",
      "Fence            571 non-null object\n",
      "MiscVal          2919 non-null int64\n",
      "MoSold           2919 non-null int64\n",
      "YrSold           2919 non-null int64\n",
      "SaleType         2919 non-null object\n",
      "SaleCondition    2919 non-null object\n",
      "dtypes: float64(11), int64(25), object(40)\n",
      "memory usage: 1.7+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = train_clean['SalePrice']\n",
    "X = train_clean.drop('SalePrice',axis=1)\n",
    "\n",
    "X_index = X.index.to_list()\n",
    "\n",
    "X_total = X.append(test_clean,ignore_index = True)\n",
    "display(X_total.info())\n",
    "\n",
    "X_test_clean_index = np.setdiff1d(X_total.index.to_list(),X_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "handed-pixel",
   "metadata": {},
   "source": [
    "# Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "twelve-fifty",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "class MultiColumnLabelEncoder:\n",
    "    def __init__(self,columns=None):\n",
    "        self.columns = columns\n",
    "    \n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self,X):\n",
    "        output = X.copy()\n",
    "        if self.columns is not None:\n",
    "            for col in self.columns:\n",
    "                output[col] = output[col].fillna('NaN')\n",
    "                output[col] = LabelEncoder().fit_transform(output[col])\n",
    "        else:\n",
    "            for colname,col in output.iteritems():\n",
    "                output[colname] = LabelEncoder().fit_transform(col)\n",
    "        return output\n",
    "    \n",
    "    def fit_transform(self,X,y=None):\n",
    "        return self.fit(X,y).transform(X)\n",
    "    \n",
    "# store the catagorical features names as a list      \n",
    "cat_features = X_total.select_dtypes(['object']).columns.to_list()\n",
    "\n",
    "# use MultiColumnLabelEncoder to apply LabelEncoding on cat_features \n",
    "# uses NaN as a value , no imputation will be used for missing data\n",
    "X_total_encoded = MultiColumnLabelEncoder(columns = cat_features).fit_transform(X_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "abroad-layer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1460 entries, 0 to 1459\n",
      "Data columns (total 76 columns):\n",
      "MSSubClass       1460 non-null int64\n",
      "MSZoning         1460 non-null int64\n",
      "LotFrontage      1460 non-null float64\n",
      "LotArea          1460 non-null int64\n",
      "Street           1460 non-null int64\n",
      "LotShape         1460 non-null int64\n",
      "LandContour      1460 non-null int64\n",
      "Utilities        1460 non-null int64\n",
      "LotConfig        1460 non-null int64\n",
      "LandSlope        1460 non-null int64\n",
      "Neighborhood     1460 non-null int64\n",
      "Condition1       1460 non-null int64\n",
      "Condition2       1460 non-null int64\n",
      "BldgType         1460 non-null int64\n",
      "HouseStyle       1460 non-null int64\n",
      "OverallQual      1460 non-null int64\n",
      "OverallCond      1460 non-null int64\n",
      "YearBuilt        1460 non-null int64\n",
      "YearRemodAdd     1460 non-null int64\n",
      "RoofStyle        1460 non-null int64\n",
      "RoofMatl         1460 non-null int64\n",
      "Exterior1st      1460 non-null int64\n",
      "Exterior2nd      1460 non-null int64\n",
      "MasVnrType       1460 non-null int64\n",
      "MasVnrArea       1460 non-null float64\n",
      "ExterQual        1460 non-null int64\n",
      "ExterCond        1460 non-null int64\n",
      "Foundation       1460 non-null int64\n",
      "BsmtQual         1460 non-null int64\n",
      "BsmtCond         1460 non-null int64\n",
      "BsmtExposure     1460 non-null int64\n",
      "BsmtFinType1     1460 non-null int64\n",
      "BsmtFinSF1       1460 non-null float64\n",
      "BsmtFinType2     1460 non-null int64\n",
      "BsmtFinSF2       1460 non-null float64\n",
      "BsmtUnfSF        1460 non-null float64\n",
      "TotalBsmtSF      1460 non-null float64\n",
      "Heating          1460 non-null int64\n",
      "HeatingQC        1460 non-null int64\n",
      "CentralAir       1460 non-null int64\n",
      "Electrical       1460 non-null int64\n",
      "1stFlrSF         1460 non-null int64\n",
      "2ndFlrSF         1460 non-null int64\n",
      "LowQualFinSF     1460 non-null int64\n",
      "GrLivArea        1460 non-null int64\n",
      "BsmtFullBath     1460 non-null float64\n",
      "BsmtHalfBath     1460 non-null float64\n",
      "FullBath         1460 non-null int64\n",
      "HalfBath         1460 non-null int64\n",
      "BedroomAbvGr     1460 non-null int64\n",
      "KitchenAbvGr     1460 non-null int64\n",
      "KitchenQual      1460 non-null int64\n",
      "TotRmsAbvGrd     1460 non-null int64\n",
      "Functional       1460 non-null int64\n",
      "Fireplaces       1460 non-null int64\n",
      "FireplaceQu      1460 non-null int64\n",
      "GarageType       1460 non-null int64\n",
      "GarageYrBlt      1460 non-null float64\n",
      "GarageFinish     1460 non-null int64\n",
      "GarageCars       1460 non-null float64\n",
      "GarageArea       1460 non-null float64\n",
      "GarageQual       1460 non-null int64\n",
      "GarageCond       1460 non-null int64\n",
      "PavedDrive       1460 non-null int64\n",
      "WoodDeckSF       1460 non-null int64\n",
      "OpenPorchSF      1460 non-null int64\n",
      "EnclosedPorch    1460 non-null int64\n",
      "3SsnPorch        1460 non-null int64\n",
      "ScreenPorch      1460 non-null int64\n",
      "PoolArea         1460 non-null int64\n",
      "Fence            1460 non-null int64\n",
      "MiscVal          1460 non-null int64\n",
      "MoSold           1460 non-null int64\n",
      "YrSold           1460 non-null int64\n",
      "SaleType         1460 non-null int64\n",
      "SaleCondition    1460 non-null int64\n",
      "dtypes: float64(11), int64(65)\n",
      "memory usage: 878.3 KB\n"
     ]
    }
   ],
   "source": [
    "##% Split X_total_encoded \n",
    "X_train_clean_encoded = X_total_encoded.iloc[X_index, :]\n",
    "X_test_clean_encoded = X_total_encoded.iloc[X_test_clean_index, :].reset_index(drop = True) \n",
    "X_train_clean_encoded.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "tutorial-fireplace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>706</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>856</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>548</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>978</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>486</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>608</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>216</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>540</td>\n",
       "      <td>756</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street LotShape LandContour  \\\n",
       "0          60       RL         65.0     8450   Pave      Reg         Lvl   \n",
       "1          20       RL         80.0     9600   Pave      Reg         Lvl   \n",
       "2          60       RL         68.0    11250   Pave      IR1         Lvl   \n",
       "3          70       RL         60.0     9550   Pave      IR1         Lvl   \n",
       "4          60       RL         84.0    14260   Pave      IR1         Lvl   \n",
       "\n",
       "  Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType  \\\n",
       "0    AllPub    Inside       Gtl      CollgCr       Norm       Norm     1Fam   \n",
       "1    AllPub       FR2       Gtl      Veenker      Feedr       Norm     1Fam   \n",
       "2    AllPub    Inside       Gtl      CollgCr       Norm       Norm     1Fam   \n",
       "3    AllPub    Corner       Gtl      Crawfor       Norm       Norm     1Fam   \n",
       "4    AllPub       FR2       Gtl      NoRidge       Norm       Norm     1Fam   \n",
       "\n",
       "  HouseStyle  OverallQual  OverallCond  YearBuilt  YearRemodAdd RoofStyle  \\\n",
       "0     2Story            7            5       2003          2003     Gable   \n",
       "1     1Story            6            8       1976          1976     Gable   \n",
       "2     2Story            7            5       2001          2002     Gable   \n",
       "3     2Story            7            5       1915          1970     Gable   \n",
       "4     2Story            8            5       2000          2000     Gable   \n",
       "\n",
       "  RoofMatl Exterior1st Exterior2nd MasVnrType  MasVnrArea ExterQual ExterCond  \\\n",
       "0  CompShg     VinylSd     VinylSd    BrkFace       196.0        Gd        TA   \n",
       "1  CompShg     MetalSd     MetalSd       None         0.0        TA        TA   \n",
       "2  CompShg     VinylSd     VinylSd    BrkFace       162.0        Gd        TA   \n",
       "3  CompShg     Wd Sdng     Wd Shng       None         0.0        TA        TA   \n",
       "4  CompShg     VinylSd     VinylSd    BrkFace       350.0        Gd        TA   \n",
       "\n",
       "  Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1  BsmtFinSF1  \\\n",
       "0      PConc       Gd       TA           No          GLQ         706   \n",
       "1     CBlock       Gd       TA           Gd          ALQ         978   \n",
       "2      PConc       Gd       TA           Mn          GLQ         486   \n",
       "3     BrkTil       TA       Gd           No          ALQ         216   \n",
       "4      PConc       Gd       TA           Av          GLQ         655   \n",
       "\n",
       "  BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF Heating HeatingQC  \\\n",
       "0          Unf           0        150          856    GasA        Ex   \n",
       "1          Unf           0        284         1262    GasA        Ex   \n",
       "2          Unf           0        434          920    GasA        Ex   \n",
       "3          Unf           0        540          756    GasA        Gd   \n",
       "4          Unf           0        490         1145    GasA        Ex   \n",
       "\n",
       "  CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  \\\n",
       "0          Y      SBrkr       856       854             0       1710   \n",
       "1          Y      SBrkr      1262         0             0       1262   \n",
       "2          Y      SBrkr       920       866             0       1786   \n",
       "3          Y      SBrkr       961       756             0       1717   \n",
       "4          Y      SBrkr      1145      1053             0       2198   \n",
       "\n",
       "   BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr  \\\n",
       "0             1             0         2         1             3             1   \n",
       "1             0             1         2         0             3             1   \n",
       "2             1             0         2         1             3             1   \n",
       "3             1             0         1         0             3             1   \n",
       "4             1             0         2         1             4             1   \n",
       "\n",
       "  KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu GarageType  \\\n",
       "0          Gd             8        Typ           0         NaN     Attchd   \n",
       "1          TA             6        Typ           1          TA     Attchd   \n",
       "2          Gd             6        Typ           1          TA     Attchd   \n",
       "3          Gd             7        Typ           1          Gd     Detchd   \n",
       "4          Gd             9        Typ           1          TA     Attchd   \n",
       "\n",
       "   GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual GarageCond  \\\n",
       "0       2003.0          RFn           2         548         TA         TA   \n",
       "1       1976.0          RFn           2         460         TA         TA   \n",
       "2       2001.0          RFn           2         608         TA         TA   \n",
       "3       1998.0          Unf           3         642         TA         TA   \n",
       "4       2000.0          RFn           3         836         TA         TA   \n",
       "\n",
       "  PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
       "0          Y           0           61              0          0            0   \n",
       "1          Y         298            0              0          0            0   \n",
       "2          Y           0           42              0          0            0   \n",
       "3          Y           0           35            272          0            0   \n",
       "4          Y         192           84              0          0            0   \n",
       "\n",
       "   PoolArea Fence  MiscVal  MoSold  YrSold SaleType SaleCondition  \n",
       "0         0   NaN        0       2    2008       WD        Normal  \n",
       "1         0   NaN        0       5    2007       WD        Normal  \n",
       "2         0   NaN        0       9    2008       WD        Normal  \n",
       "3         0   NaN        0       2    2006       WD       Abnorml  \n",
       "4         0   NaN        0      12    2008       WD        Normal  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>196.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>706.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>856.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>486.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>350.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>655.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  MSZoning  LotFrontage  LotArea  Street  LotShape  LandContour  \\\n",
       "0          60         3         65.0     8450       1         3            3   \n",
       "1          20         3         80.0     9600       1         3            3   \n",
       "2          60         3         68.0    11250       1         0            3   \n",
       "3          70         3         60.0     9550       1         0            3   \n",
       "4          60         3         84.0    14260       1         0            3   \n",
       "\n",
       "   Utilities  LotConfig  LandSlope  Neighborhood  Condition1  Condition2  \\\n",
       "0          0          4          0             5           2           2   \n",
       "1          0          2          0            24           1           2   \n",
       "2          0          4          0             5           2           2   \n",
       "3          0          0          0             6           2           2   \n",
       "4          0          2          0            15           2           2   \n",
       "\n",
       "   BldgType  HouseStyle  OverallQual  OverallCond  YearBuilt  YearRemodAdd  \\\n",
       "0         0           5            7            5       2003          2003   \n",
       "1         0           2            6            8       1976          1976   \n",
       "2         0           5            7            5       2001          2002   \n",
       "3         0           5            7            5       1915          1970   \n",
       "4         0           5            8            5       2000          2000   \n",
       "\n",
       "   RoofStyle  RoofMatl  Exterior1st  Exterior2nd  MasVnrType  MasVnrArea  \\\n",
       "0          1         1           12           13           1       196.0   \n",
       "1          1         1            8            8           2         0.0   \n",
       "2          1         1           12           13           1       162.0   \n",
       "3          1         1           13           15           2         0.0   \n",
       "4          1         1           12           13           1       350.0   \n",
       "\n",
       "   ExterQual  ExterCond  Foundation  BsmtQual  BsmtCond  BsmtExposure  \\\n",
       "0          2          4           2         2         3             3   \n",
       "1          3          4           1         2         3             1   \n",
       "2          2          4           2         2         3             2   \n",
       "3          3          4           0         3         1             3   \n",
       "4          2          4           2         2         3             0   \n",
       "\n",
       "   BsmtFinType1  BsmtFinSF1  BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
       "0             2       706.0             5         0.0      150.0        856.0   \n",
       "1             0       978.0             5         0.0      284.0       1262.0   \n",
       "2             2       486.0             5         0.0      434.0        920.0   \n",
       "3             0       216.0             5         0.0      540.0        756.0   \n",
       "4             2       655.0             5         0.0      490.0       1145.0   \n",
       "\n",
       "   Heating  HeatingQC  CentralAir  Electrical  1stFlrSF  2ndFlrSF  \\\n",
       "0        1          0           1           4       856       854   \n",
       "1        1          0           1           4      1262         0   \n",
       "2        1          0           1           4       920       866   \n",
       "3        1          2           1           4       961       756   \n",
       "4        1          0           1           4      1145      1053   \n",
       "\n",
       "   LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  \\\n",
       "0             0       1710           1.0           0.0         2         1   \n",
       "1             0       1262           0.0           1.0         2         0   \n",
       "2             0       1786           1.0           0.0         2         1   \n",
       "3             0       1717           1.0           0.0         1         0   \n",
       "4             0       2198           1.0           0.0         2         1   \n",
       "\n",
       "   BedroomAbvGr  KitchenAbvGr  KitchenQual  TotRmsAbvGrd  Functional  \\\n",
       "0             3             1            2             8           6   \n",
       "1             3             1            3             6           6   \n",
       "2             3             1            2             6           6   \n",
       "3             3             1            2             7           6   \n",
       "4             4             1            2             9           6   \n",
       "\n",
       "   Fireplaces  FireplaceQu  GarageType  GarageYrBlt  GarageFinish  GarageCars  \\\n",
       "0           0            3           1       2003.0             1         2.0   \n",
       "1           1            5           1       1976.0             1         2.0   \n",
       "2           1            5           1       2001.0             1         2.0   \n",
       "3           1            2           5       1998.0             2         3.0   \n",
       "4           1            5           1       2000.0             1         3.0   \n",
       "\n",
       "   GarageArea  GarageQual  GarageCond  PavedDrive  WoodDeckSF  OpenPorchSF  \\\n",
       "0       548.0           4           4           2           0           61   \n",
       "1       460.0           4           4           2         298            0   \n",
       "2       608.0           4           4           2           0           42   \n",
       "3       642.0           4           4           2           0           35   \n",
       "4       836.0           4           4           2         192           84   \n",
       "\n",
       "   EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  Fence  MiscVal  MoSold  \\\n",
       "0              0          0            0         0      4        0       2   \n",
       "1              0          0            0         0      4        0       5   \n",
       "2              0          0            0         0      4        0       9   \n",
       "3            272          0            0         0      4        0       2   \n",
       "4              0          0            0         0      4        0      12   \n",
       "\n",
       "   YrSold  SaleType  SaleCondition  \n",
       "0    2008         8              4  \n",
       "1    2007         8              4  \n",
       "2    2008         8              4  \n",
       "3    2006         8              0  \n",
       "4    2008         8              4  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##% Before and After LabelEncoding for train.csv \n",
    "display(X.head())\n",
    "display(X_train_clean_encoded.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "missing-reviewer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Rec</td>\n",
       "      <td>468.0</td>\n",
       "      <td>LwQ</td>\n",
       "      <td>144.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>896</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>108.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>923.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1329</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>393</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>791.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>928</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>1629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>212</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>20.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>602.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>926</td>\n",
       "      <td>678</td>\n",
       "      <td>0</td>\n",
       "      <td>1604</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>360</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>StoneBr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>TwnhsE</td>\n",
       "      <td>1Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>263.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street LotShape LandContour  \\\n",
       "0          20       RH         80.0    11622   Pave      Reg         Lvl   \n",
       "1          20       RL         81.0    14267   Pave      IR1         Lvl   \n",
       "2          60       RL         74.0    13830   Pave      IR1         Lvl   \n",
       "3          60       RL         78.0     9978   Pave      IR1         Lvl   \n",
       "4         120       RL         43.0     5005   Pave      IR1         HLS   \n",
       "\n",
       "  Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType  \\\n",
       "0    AllPub    Inside       Gtl        NAmes      Feedr       Norm     1Fam   \n",
       "1    AllPub    Corner       Gtl        NAmes       Norm       Norm     1Fam   \n",
       "2    AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam   \n",
       "3    AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam   \n",
       "4    AllPub    Inside       Gtl      StoneBr       Norm       Norm   TwnhsE   \n",
       "\n",
       "  HouseStyle  OverallQual  OverallCond  YearBuilt  YearRemodAdd RoofStyle  \\\n",
       "0     1Story            5            6       1961          1961     Gable   \n",
       "1     1Story            6            6       1958          1958       Hip   \n",
       "2     2Story            5            5       1997          1998     Gable   \n",
       "3     2Story            6            6       1998          1998     Gable   \n",
       "4     1Story            8            5       1992          1992     Gable   \n",
       "\n",
       "  RoofMatl Exterior1st Exterior2nd MasVnrType  MasVnrArea ExterQual ExterCond  \\\n",
       "0  CompShg     VinylSd     VinylSd       None         0.0        TA        TA   \n",
       "1  CompShg     Wd Sdng     Wd Sdng    BrkFace       108.0        TA        TA   \n",
       "2  CompShg     VinylSd     VinylSd       None         0.0        TA        TA   \n",
       "3  CompShg     VinylSd     VinylSd    BrkFace        20.0        TA        TA   \n",
       "4  CompShg     HdBoard     HdBoard       None         0.0        Gd        TA   \n",
       "\n",
       "  Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1  BsmtFinSF1  \\\n",
       "0     CBlock       TA       TA           No          Rec       468.0   \n",
       "1     CBlock       TA       TA           No          ALQ       923.0   \n",
       "2      PConc       Gd       TA           No          GLQ       791.0   \n",
       "3      PConc       TA       TA           No          GLQ       602.0   \n",
       "4      PConc       Gd       TA           No          ALQ       263.0   \n",
       "\n",
       "  BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF Heating HeatingQC  \\\n",
       "0          LwQ       144.0      270.0        882.0    GasA        TA   \n",
       "1          Unf         0.0      406.0       1329.0    GasA        TA   \n",
       "2          Unf         0.0      137.0        928.0    GasA        Gd   \n",
       "3          Unf         0.0      324.0        926.0    GasA        Ex   \n",
       "4          Unf         0.0     1017.0       1280.0    GasA        Ex   \n",
       "\n",
       "  CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  \\\n",
       "0          Y      SBrkr       896         0             0        896   \n",
       "1          Y      SBrkr      1329         0             0       1329   \n",
       "2          Y      SBrkr       928       701             0       1629   \n",
       "3          Y      SBrkr       926       678             0       1604   \n",
       "4          Y      SBrkr      1280         0             0       1280   \n",
       "\n",
       "   BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr  \\\n",
       "0           0.0           0.0         1         0             2             1   \n",
       "1           0.0           0.0         1         1             3             1   \n",
       "2           0.0           0.0         2         1             3             1   \n",
       "3           0.0           0.0         2         1             3             1   \n",
       "4           0.0           0.0         2         0             2             1   \n",
       "\n",
       "  KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu GarageType  \\\n",
       "0          TA             5        Typ           0         NaN     Attchd   \n",
       "1          Gd             6        Typ           0         NaN     Attchd   \n",
       "2          TA             6        Typ           1          TA     Attchd   \n",
       "3          Gd             7        Typ           1          Gd     Attchd   \n",
       "4          Gd             5        Typ           0         NaN     Attchd   \n",
       "\n",
       "   GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual GarageCond  \\\n",
       "0       1961.0          Unf         1.0       730.0         TA         TA   \n",
       "1       1958.0          Unf         1.0       312.0         TA         TA   \n",
       "2       1997.0          Fin         2.0       482.0         TA         TA   \n",
       "3       1998.0          Fin         2.0       470.0         TA         TA   \n",
       "4       1992.0          RFn         2.0       506.0         TA         TA   \n",
       "\n",
       "  PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
       "0          Y         140            0              0          0          120   \n",
       "1          Y         393           36              0          0            0   \n",
       "2          Y         212           34              0          0            0   \n",
       "3          Y         360           36              0          0            0   \n",
       "4          Y           0           82              0          0          144   \n",
       "\n",
       "   PoolArea  Fence  MiscVal  MoSold  YrSold SaleType SaleCondition  \n",
       "0         0  MnPrv        0       6    2010       WD        Normal  \n",
       "1         0    NaN    12500       6    2010       WD        Normal  \n",
       "2         0  MnPrv        0       3    2010       WD        Normal  \n",
       "3         0    NaN        0       6    2010       WD        Normal  \n",
       "4         0    NaN        0       1    2010       WD        Normal  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>468.0</td>\n",
       "      <td>3</td>\n",
       "      <td>144.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>896</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>108.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1329</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>393</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>791.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>928</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>1629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>212</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>602.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>926</td>\n",
       "      <td>678</td>\n",
       "      <td>0</td>\n",
       "      <td>1604</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>360</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120</td>\n",
       "      <td>3</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  MSZoning  LotFrontage  LotArea  Street  LotShape  LandContour  \\\n",
       "0          20         2         80.0    11622       1         3            3   \n",
       "1          20         3         81.0    14267       1         0            3   \n",
       "2          60         3         74.0    13830       1         0            3   \n",
       "3          60         3         78.0     9978       1         0            3   \n",
       "4         120         3         43.0     5005       1         0            1   \n",
       "\n",
       "   Utilities  LotConfig  LandSlope  Neighborhood  Condition1  Condition2  \\\n",
       "0          0          4          0            12           1           2   \n",
       "1          0          0          0            12           2           2   \n",
       "2          0          4          0             8           2           2   \n",
       "3          0          4          0             8           2           2   \n",
       "4          0          4          0            22           2           2   \n",
       "\n",
       "   BldgType  HouseStyle  OverallQual  OverallCond  YearBuilt  YearRemodAdd  \\\n",
       "0         0           2            5            6       1961          1961   \n",
       "1         0           2            6            6       1958          1958   \n",
       "2         0           5            5            5       1997          1998   \n",
       "3         0           5            6            6       1998          1998   \n",
       "4         4           2            8            5       1992          1992   \n",
       "\n",
       "   RoofStyle  RoofMatl  Exterior1st  Exterior2nd  MasVnrType  MasVnrArea  \\\n",
       "0          1         1           12           13           2         0.0   \n",
       "1          3         1           13           14           1       108.0   \n",
       "2          1         1           12           13           2         0.0   \n",
       "3          1         1           12           13           1        20.0   \n",
       "4          1         1            6            6           2         0.0   \n",
       "\n",
       "   ExterQual  ExterCond  Foundation  BsmtQual  BsmtCond  BsmtExposure  \\\n",
       "0          3          4           1         3         3             3   \n",
       "1          3          4           1         3         3             3   \n",
       "2          3          4           2         2         3             3   \n",
       "3          3          4           2         3         3             3   \n",
       "4          2          4           2         2         3             3   \n",
       "\n",
       "   BsmtFinType1  BsmtFinSF1  BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
       "0             4       468.0             3       144.0      270.0        882.0   \n",
       "1             0       923.0             5         0.0      406.0       1329.0   \n",
       "2             2       791.0             5         0.0      137.0        928.0   \n",
       "3             2       602.0             5         0.0      324.0        926.0   \n",
       "4             0       263.0             5         0.0     1017.0       1280.0   \n",
       "\n",
       "   Heating  HeatingQC  CentralAir  Electrical  1stFlrSF  2ndFlrSF  \\\n",
       "0        1          4           1           4       896         0   \n",
       "1        1          4           1           4      1329         0   \n",
       "2        1          2           1           4       928       701   \n",
       "3        1          0           1           4       926       678   \n",
       "4        1          0           1           4      1280         0   \n",
       "\n",
       "   LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  \\\n",
       "0             0        896           0.0           0.0         1         0   \n",
       "1             0       1329           0.0           0.0         1         1   \n",
       "2             0       1629           0.0           0.0         2         1   \n",
       "3             0       1604           0.0           0.0         2         1   \n",
       "4             0       1280           0.0           0.0         2         0   \n",
       "\n",
       "   BedroomAbvGr  KitchenAbvGr  KitchenQual  TotRmsAbvGrd  Functional  \\\n",
       "0             2             1            3             5           6   \n",
       "1             3             1            2             6           6   \n",
       "2             3             1            3             6           6   \n",
       "3             3             1            2             7           6   \n",
       "4             2             1            2             5           6   \n",
       "\n",
       "   Fireplaces  FireplaceQu  GarageType  GarageYrBlt  GarageFinish  GarageCars  \\\n",
       "0           0            3           1       1961.0             2         1.0   \n",
       "1           0            3           1       1958.0             2         1.0   \n",
       "2           1            5           1       1997.0             0         2.0   \n",
       "3           1            2           1       1998.0             0         2.0   \n",
       "4           0            3           1       1992.0             1         2.0   \n",
       "\n",
       "   GarageArea  GarageQual  GarageCond  PavedDrive  WoodDeckSF  OpenPorchSF  \\\n",
       "0       730.0           4           4           2         140            0   \n",
       "1       312.0           4           4           2         393           36   \n",
       "2       482.0           4           4           2         212           34   \n",
       "3       470.0           4           4           2         360           36   \n",
       "4       506.0           4           4           2           0           82   \n",
       "\n",
       "   EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  Fence  MiscVal  MoSold  \\\n",
       "0              0          0          120         0      2        0       6   \n",
       "1              0          0            0         0      4    12500       6   \n",
       "2              0          0            0         0      2        0       3   \n",
       "3              0          0            0         0      4        0       6   \n",
       "4              0          0          144         0      4        0       1   \n",
       "\n",
       "   YrSold  SaleType  SaleCondition  \n",
       "0    2010         8              4  \n",
       "1    2010         8              4  \n",
       "2    2010         8              4  \n",
       "3    2010         8              4  \n",
       "4    2010         8              4  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##% Before and After LabelEncoding for test.csv \n",
    "display(test_clean.head())\n",
    "display(X_test_clean_encoded.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "manual-source",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1168 entries, 618 to 684\n",
      "Data columns (total 76 columns):\n",
      "MSSubClass       1168 non-null int64\n",
      "MSZoning         1168 non-null int64\n",
      "LotFrontage      1168 non-null float64\n",
      "LotArea          1168 non-null int64\n",
      "Street           1168 non-null int64\n",
      "LotShape         1168 non-null int64\n",
      "LandContour      1168 non-null int64\n",
      "Utilities        1168 non-null int64\n",
      "LotConfig        1168 non-null int64\n",
      "LandSlope        1168 non-null int64\n",
      "Neighborhood     1168 non-null int64\n",
      "Condition1       1168 non-null int64\n",
      "Condition2       1168 non-null int64\n",
      "BldgType         1168 non-null int64\n",
      "HouseStyle       1168 non-null int64\n",
      "OverallQual      1168 non-null int64\n",
      "OverallCond      1168 non-null int64\n",
      "YearBuilt        1168 non-null int64\n",
      "YearRemodAdd     1168 non-null int64\n",
      "RoofStyle        1168 non-null int64\n",
      "RoofMatl         1168 non-null int64\n",
      "Exterior1st      1168 non-null int64\n",
      "Exterior2nd      1168 non-null int64\n",
      "MasVnrType       1168 non-null int64\n",
      "MasVnrArea       1168 non-null float64\n",
      "ExterQual        1168 non-null int64\n",
      "ExterCond        1168 non-null int64\n",
      "Foundation       1168 non-null int64\n",
      "BsmtQual         1168 non-null int64\n",
      "BsmtCond         1168 non-null int64\n",
      "BsmtExposure     1168 non-null int64\n",
      "BsmtFinType1     1168 non-null int64\n",
      "BsmtFinSF1       1168 non-null float64\n",
      "BsmtFinType2     1168 non-null int64\n",
      "BsmtFinSF2       1168 non-null float64\n",
      "BsmtUnfSF        1168 non-null float64\n",
      "TotalBsmtSF      1168 non-null float64\n",
      "Heating          1168 non-null int64\n",
      "HeatingQC        1168 non-null int64\n",
      "CentralAir       1168 non-null int64\n",
      "Electrical       1168 non-null int64\n",
      "1stFlrSF         1168 non-null int64\n",
      "2ndFlrSF         1168 non-null int64\n",
      "LowQualFinSF     1168 non-null int64\n",
      "GrLivArea        1168 non-null int64\n",
      "BsmtFullBath     1168 non-null float64\n",
      "BsmtHalfBath     1168 non-null float64\n",
      "FullBath         1168 non-null int64\n",
      "HalfBath         1168 non-null int64\n",
      "BedroomAbvGr     1168 non-null int64\n",
      "KitchenAbvGr     1168 non-null int64\n",
      "KitchenQual      1168 non-null int64\n",
      "TotRmsAbvGrd     1168 non-null int64\n",
      "Functional       1168 non-null int64\n",
      "Fireplaces       1168 non-null int64\n",
      "FireplaceQu      1168 non-null int64\n",
      "GarageType       1168 non-null int64\n",
      "GarageYrBlt      1168 non-null float64\n",
      "GarageFinish     1168 non-null int64\n",
      "GarageCars       1168 non-null float64\n",
      "GarageArea       1168 non-null float64\n",
      "GarageQual       1168 non-null int64\n",
      "GarageCond       1168 non-null int64\n",
      "PavedDrive       1168 non-null int64\n",
      "WoodDeckSF       1168 non-null int64\n",
      "OpenPorchSF      1168 non-null int64\n",
      "EnclosedPorch    1168 non-null int64\n",
      "3SsnPorch        1168 non-null int64\n",
      "ScreenPorch      1168 non-null int64\n",
      "PoolArea         1168 non-null int64\n",
      "Fence            1168 non-null int64\n",
      "MiscVal          1168 non-null int64\n",
      "MoSold           1168 non-null int64\n",
      "YrSold           1168 non-null int64\n",
      "SaleType         1168 non-null int64\n",
      "SaleCondition    1168 non-null int64\n",
      "dtypes: float64(11), int64(65)\n",
      "memory usage: 702.6 KB\n"
     ]
    }
   ],
   "source": [
    "# Create test and train set 80-20\n",
    "#%%  train-test split using a 80-20 split\n",
    "train_X, valid_X, train_y, valid_y = train_test_split(X_train_clean_encoded, y, test_size=0.2, shuffle = True, random_state=0)\n",
    "\n",
    "train_X.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "occupied-contributor",
   "metadata": {},
   "source": [
    "# Initial Models\n",
    "We can not apply different machine learning algorthims to test which model perform better on this dataset. I've listed below various machine learning techniques applied in this section.\n",
    "\n",
    "*   RandomForest\n",
    "*   Support Vector Machine\n",
    "*   XGBoost\n",
    "*   LightGBM\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "laden-domain",
   "metadata": {},
   "outputs": [],
   "source": [
    "##% evaluateRegressor\n",
    "# from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "def evaluateRegressor(true,predicted,message = \"Test set\"):\n",
    "    MSE = mse(true,predicted,squared = True)\n",
    "    MAE = mae(true,predicted)\n",
    "    RMSE = mse(true,predicted,squared = False)\n",
    "    LogRMSE = mse(np.log(true),np.log(predicted),squared = False)\n",
    "    print(message)\n",
    "    print(\"MSE:\", MSE)\n",
    "    print(\"MAE:\", MAE)\n",
    "    print(\"RMSE:\", RMSE)\n",
    "    print(\"LogRMSE:\", LogRMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "about-divorce",
   "metadata": {},
   "outputs": [],
   "source": [
    "##% Plot True vs predicted values. Useful for continuous y \n",
    "def PlotPrediction(true,predicted, title = \"Dataset: \"):\n",
    "    fig = plt.figure(figsize=(20,20))\n",
    "    ax1 = fig.add_subplot(111)\n",
    "    ax1.set_title(title + 'True vs Predicted')\n",
    "    ax1.scatter(list(range(0,len(true))),true, s=10, c='r', marker=\"o\", label='True')\n",
    "    ax1.scatter(list(range(0,len(predicted))), predicted, s=10, c='b', marker=\"o\", label='Predicted')\n",
    "    plt.legend(loc='upper right');\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "pharmaceutical-jones",
   "metadata": {},
   "outputs": [],
   "source": [
    "##% Initial Models\n",
    "# from sklearn.ensemble import RandomForestRegressor\n",
    "# from sklearn import svm\n",
    "# import lightgbm as lgb\n",
    "# import xgboost as xg \n",
    "\n",
    "RFReg = RandomForestRegressor(random_state = 0).fit(train_X, train_y)\n",
    "SVM = svm.SVR().fit(train_X, train_y) \n",
    "XGReg = xg.XGBRegressor(objective ='reg:squarederror', seed = 0,verbosity=0).fit(train_X,train_y) \n",
    "LGBMReg = lgb.LGBMRegressor(random_state=0).fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fluid-wheat",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regressor\n",
      "    Training Set\n",
      "MSE: 124458317.90507004\n",
      "MAE: 6553.9395547945205\n",
      "RMSE: 11156.088826513978\n",
      "LogRMSE: 0.06263755839008012\n",
      "    Test Set\n",
      "MSE: 1069955990.5817266\n",
      "MAE: 17148.310719178084\n",
      "RMSE: 32710.181757087907\n",
      "LogRMSE: 0.1387003405605433\n",
      "\n",
      "\n",
      "Support Vector Machine\n",
      "    Training Set\n",
      "MSE: 6472139002.468355\n",
      "MAE: 55516.23203243733\n",
      "RMSE: 80449.60535930774\n",
      "LogRMSE: 0.4023290326424375\n",
      "    Test Set\n",
      "MSE: 7241308930.447164\n",
      "MAE: 55542.95134930656\n",
      "RMSE: 85095.88080775217\n",
      "LogRMSE: 0.38983624335105826\n",
      "\n",
      "\n",
      "XGBoost Regressor\n",
      "    Training Set\n",
      "MSE: 1146598.1193553323\n",
      "MAE: 733.3596325181935\n",
      "RMSE: 1070.7932196999252\n",
      "LogRMSE: 0.00719356591327356\n",
      "    Test Set\n",
      "MSE: 1033421355.0116786\n",
      "MAE: 17563.705586472603\n",
      "RMSE: 32146.871620916376\n",
      "LogRMSE: 0.13606965265867998\n",
      "\n",
      "\n",
      "LightGBM Regressor\n",
      "    Training Set\n",
      "MSE: 126288500.57971074\n",
      "MAE: 5013.819061606673\n",
      "RMSE: 11237.815649836526\n",
      "LogRMSE: 0.04809269887204899\n",
      "    Test Set\n",
      "MSE: 999264659.8651305\n",
      "MAE: 16598.163380637183\n",
      "RMSE: 31611.147715088275\n",
      "LogRMSE: 0.13558250786123766\n"
     ]
    }
   ],
   "source": [
    "##% Model Metrics\n",
    "print(\"Random Forest Regressor\") \n",
    "predicted_train_y = RFReg.predict(train_X)\n",
    "evaluateRegressor(train_y,predicted_train_y,\"    Training Set\")\n",
    "predicted_valid_y = RFReg.predict(valid_X)\n",
    "evaluateRegressor(valid_y,predicted_valid_y,\"    Test Set\")\n",
    "print(\"\\n\")\n",
    "    \n",
    "print(\"Support Vector Machine\") \n",
    "predicted_train_y = SVM.predict(train_X)\n",
    "evaluateRegressor(train_y,predicted_train_y,\"    Training Set\")\n",
    "predicted_valid_y = SVM.predict(valid_X)\n",
    "evaluateRegressor(valid_y,predicted_valid_y,\"    Test Set\")\n",
    "print(\"\\n\")\n",
    "\n",
    "\n",
    "print(\"XGBoost Regressor\") \n",
    "predicted_train_y = XGReg.predict(train_X)\n",
    "evaluateRegressor(train_y,predicted_train_y,\"    Training Set\")\n",
    "predicted_valid_y = XGReg.predict(valid_X)\n",
    "evaluateRegressor(valid_y,predicted_valid_y,\"    Test Set\")\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"LightGBM Regressor\") \n",
    "predicted_train_y = LGBMReg.predict(train_X)\n",
    "evaluateRegressor(train_y,predicted_train_y,\"    Training Set\")\n",
    "predicted_valid_y = LGBMReg.predict(valid_X)\n",
    "evaluateRegressor(valid_y,predicted_valid_y,\"    Test Set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "motivated-surveillance",
   "metadata": {},
   "outputs": [],
   "source": [
    "##% parameter tuning for lightgbm \n",
    "# store the catagorical features names as a list      \n",
    "cat_features = X_train_clean_encoded.select_dtypes(['object']).columns.to_list()\n",
    "\n",
    "# Create the LightGBM data containers\n",
    "# Make sure that cat_features are used\n",
    "train_data=lgb.Dataset(train_X,label=train_y, categorical_feature = cat_features,free_raw_data=False)\n",
    "valid_data=lgb.Dataset(valid_X,label=valid_y, categorical_feature = cat_features,free_raw_data=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "clean-advance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://medium.com/analytics-vidhya/hyperparameters-optimization-for-lightgbm-catboost-and-xgboost-regressors-using-bayesian-6e7c495947a9\n",
    "# from lightgbm import LGBMRegressor \n",
    "# from bayes_opt import BayesianOptimization\n",
    "def search_best_param(X,y,cat_features):\n",
    "    \n",
    "    trainXY = lgb.Dataset(data=X, label=y,categorical_feature = cat_features,free_raw_data=False)\n",
    "    # define the lightGBM cross validation\n",
    "    def lightGBM_CV(max_depth, num_leaves, n_estimators, learning_rate, subsample, colsample_bytree, \n",
    "                lambda_l1, lambda_l2, min_child_weight):        \n",
    "        params = {'boosting_type': 'gbdt', 'objective': 'regression', 'metric':'rmse', 'verbose': -1,\n",
    "                  'early_stopping_round':100}\n",
    "        \n",
    "        params['max_depth'] = int(round(max_depth))\n",
    "        params[\"num_leaves\"] = int(round(num_leaves))\n",
    "        params[\"n_estimators\"] = int(round(n_estimators))\n",
    "        params['learning_rate'] = learning_rate\n",
    "        params['subsample'] = subsample\n",
    "        params['colsample_bytree'] = colsample_bytree\n",
    "        params['lambda_l1'] = max(lambda_l1, 0)\n",
    "        params['lambda_l2'] = max(lambda_l2, 0)\n",
    "        params['min_child_weight'] = min_child_weight\n",
    "    \n",
    "        score = lgb.cv(params, trainXY, nfold=5, seed=1, stratified=False, verbose_eval =False, metrics=['rmse'])\n",
    "\n",
    "        return -np.min(score['rmse-mean']) # min or max can change best_param\n",
    "    \n",
    "    # use bayesian optimization to search for the best hyper-parameter combination\n",
    "    lightGBM_Bo = BayesianOptimization(lightGBM_CV, \n",
    "                                       {\n",
    "                                          'max_depth': (5, 50),\n",
    "                                          'num_leaves': (20, 100),\n",
    "                                          'n_estimators': (50, 1000),\n",
    "                                          'learning_rate': (0.01, 0.3),\n",
    "                                          'subsample': (0.7, 0.8),\n",
    "                                          'colsample_bytree' :(0.5, 0.99),\n",
    "                                          'lambda_l1': (0, 5),\n",
    "                                          'lambda_l2': (0, 3),\n",
    "                                          'min_child_weight': (2, 50) \n",
    "                                      },\n",
    "                                       random_state = 1,\n",
    "                                       verbose = 0\n",
    "                                      )\n",
    "    np.random.seed(1)\n",
    "    \n",
    "    lightGBM_Bo.maximize(init_points=5, n_iter=25) \n",
    "    \n",
    "    params_set = lightGBM_Bo.max['params']\n",
    "\n",
    "    # get the params of the maximum target     \n",
    "    max_target = -np.inf\n",
    "    for i in lightGBM_Bo.res: # loop thru all the residuals \n",
    "        if i['target'] > max_target:\n",
    "            params_set = i['params']\n",
    "            max_target = i['target']\n",
    "    \n",
    "    params_set.update({'verbose': -1})\n",
    "    params_set.update({'metric': 'rmse'})\n",
    "    params_set.update({'boosting_type': 'gbdt'})\n",
    "    params_set.update({'objective': 'regression'})\n",
    "    \n",
    "    params_set['max_depth'] = int(round(params_set['max_depth']))\n",
    "    params_set['num_leaves'] = int(round(params_set['num_leaves']))\n",
    "    params_set['n_estimators'] = int(round(params_set['n_estimators']))\n",
    "    params_set['seed'] = 1 #set seed\n",
    "    \n",
    "    return params_set\n",
    "\n",
    "best_params = search_best_param(train_X,train_y,cat_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "handmade-assessment",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "colsample_bytree  :  0.5989221937214203\n",
      "lambda_l1  :  2.8997706079402836\n",
      "lambda_l2  :  0.2975532330441406\n",
      "learning_rate  :  0.08836383670131573\n",
      "max_depth  :  22\n",
      "min_child_weight  :  13.233710226135237\n",
      "n_estimators  :  291\n",
      "num_leaves  :  81\n",
      "subsample  :  0.7441667823186404\n",
      "verbose  :  -1\n",
      "metric  :  rmse\n",
      "boosting_type  :  gbdt\n",
      "objective  :  regression\n",
      "seed  :  1\n"
     ]
    }
   ],
   "source": [
    "# Print best_params\n",
    "for key, value in best_params.items():\n",
    "    print(key, ' : ', value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "vocational-victoria",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tvalid_0's rmse: 31660.1\n",
      "[200]\tvalid_0's rmse: 30226.8\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[291]\tvalid_0's rmse: 29334.6\n"
     ]
    }
   ],
   "source": [
    "# Train lgbm_best using the best params found from Bayesian Optimization\n",
    "lgbm_best = lgb.train(best_params,\n",
    "                 train_data,\n",
    "                 num_boost_round = 2500,\n",
    "                 valid_sets = valid_data,\n",
    "                 early_stopping_rounds = 200,\n",
    "                 verbose_eval = 100\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "identical-heating",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM Regressor Tuned\n",
      "    Training Set\n",
      "MSE: 34100573.96234323\n",
      "MAE: 1628.1406485724908\n",
      "RMSE: 5839.569672702196\n",
      "LogRMSE: 0.022138848284310925\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"LightGBM Regressor Tuned\") \n",
    "predicted_train_y = lgbm_best.predict(train_X)\n",
    "evaluateRegressor(train_y,predicted_train_y,\"    Training Set\")\n",
    "PlotPrediction(train_y,predicted_train_y,\"Training Set: \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "structured-supply",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Test Set\n",
      "MSE: 860519761.7932965\n",
      "MAE: 16505.55157518033\n",
      "RMSE: 29334.61712368676\n",
      "LogRMSE: 0.13311820156427817\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted_valid_y = lgbm_best.predict(valid_X)\n",
    "evaluateRegressor(valid_y,predicted_valid_y,\"    Test Set\")\n",
    "PlotPrediction(valid_y,predicted_valid_y,\"Test Set: \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "surrounded-attendance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f0c3f6650>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##% Feature Importance \n",
    "# https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html\n",
    "lgb.plot_importance(lgbm_best,figsize=(25,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "macro-alias",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##% Feature Importance using shap package \n",
    "# import shap\n",
    "lgbm_best.params['objective'] = 'regression'\n",
    "shap_values = shap.TreeExplainer(lgbm_best).shap_values(valid_X)\n",
    "shap.summary_plot(shap_values, valid_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "centered-monaco",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     model0\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tvalid_0's rmse: 30773.6\n",
      "[200]\tvalid_0's rmse: 29447.6\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[223]\tvalid_0's rmse: 29191.8\n",
      "Train set logRMSE: 0.03606396858542673\n",
      " Test set logRMSE: 0.1295861111747427\n",
      "\n",
      "\n",
      "     model1\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tvalid_0's rmse: 22539.7\n",
      "[200]\tvalid_0's rmse: 23456.9\n",
      "Early stopping, best iteration is:\n",
      "[72]\tvalid_0's rmse: 22186.1\n",
      "Train set logRMSE: 0.07272460908954825\n",
      " Test set logRMSE: 0.1153392995712747\n",
      "\n",
      "\n",
      "     model2\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tvalid_0's rmse: 34014.7\n",
      "[200]\tvalid_0's rmse: 33404\n",
      "[300]\tvalid_0's rmse: 33561.3\n",
      "Early stopping, best iteration is:\n",
      "[172]\tvalid_0's rmse: 33305\n",
      "Train set logRMSE: 0.08913521435095606\n",
      " Test set logRMSE: 0.1729437758430952\n",
      "\n",
      "\n",
      "     model3\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tvalid_0's rmse: 28147\n",
      "[200]\tvalid_0's rmse: 27278.2\n",
      "[300]\tvalid_0's rmse: 26681.1\n",
      "[400]\tvalid_0's rmse: 26393.2\n",
      "[500]\tvalid_0's rmse: 26141.1\n",
      "[600]\tvalid_0's rmse: 25949.7\n",
      "[700]\tvalid_0's rmse: 25877\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[760]\tvalid_0's rmse: 25820\n",
      "Train set logRMSE: 0.00906617674244269\n",
      " Test set logRMSE: 0.11934193745125966\n",
      "\n",
      "\n",
      "     model4\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tvalid_0's rmse: 21970.5\n",
      "[200]\tvalid_0's rmse: 22826.8\n",
      "Early stopping, best iteration is:\n",
      "[46]\tvalid_0's rmse: 21099.9\n",
      "Train set logRMSE: 0.0927224424432279\n",
      " Test set logRMSE: 0.11964739700497196\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Cross Validation with LightGBM\n",
    "\n",
    "def K_Fold_LightGBM(X_train, y_train , cat_features, num_folds = 3):\n",
    "    num = 0\n",
    "    models = []\n",
    "    folds = KFold(n_splits=num_folds, shuffle=True, random_state=0)\n",
    "\n",
    "        # 5 times \n",
    "    for n_fold, (train_idx, valid_idx) in enumerate (folds.split(X_train, y_train)):\n",
    "        print(f\"     model{num}\")\n",
    "        train_X, train_y = X_train.iloc[train_idx], y_train.iloc[train_idx]\n",
    "        valid_X, valid_y = X_train.iloc[valid_idx], y_train.iloc[valid_idx]\n",
    "        \n",
    "        train_data=lgb.Dataset(train_X,label=train_y, categorical_feature = cat_features,free_raw_data=False)\n",
    "        valid_data=lgb.Dataset(valid_X,label=valid_y, categorical_feature = cat_features,free_raw_data=False)\n",
    "        \n",
    "        params_set = search_best_param(train_X,train_y,cat_features)\n",
    "\n",
    "        CV_LGBM = lgb.train(params_set,\n",
    "                            train_data,\n",
    "                            num_boost_round = 2500,\n",
    "                            valid_sets = valid_data,\n",
    "                            early_stopping_rounds = 200,\n",
    "                            verbose_eval = 100\n",
    "                           )\n",
    "        \n",
    "        # increase early_stopping_rounds can lead to overfitting \n",
    "        models.append(CV_LGBM)\n",
    "        \n",
    "        print(\"Train set logRMSE:\", mse(np.log(train_y),np.log(models[num].predict(train_X)),squared = False))\n",
    "        print(\" Test set logRMSE:\", mse(np.log(valid_y),np.log(models[num].predict(valid_X)),squared = False))\n",
    "        print(\"\\n\")\n",
    "        num = num + 1\n",
    "        \n",
    "    return models\n",
    "\n",
    "lgbm_models = K_Fold_LightGBM(X_train_clean_encoded,y,cat_features,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "everyday-elevation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict y_prds using models from cross validation \n",
    "def predict_cv(models_cv,X):\n",
    "    y_preds = np.zeros(shape = X.shape[0])\n",
    "    for model in models_cv:\n",
    "        y_preds += model.predict(X)\n",
    "        \n",
    "    return y_preds/len(models_cv)\n",
    "\n",
    "# evalute model using the entire dataset from Train.csv\n",
    "evaluateRegressor(y,predict_cv(lgbm_models,X_train_clean_encoded),\"Train.csv \")\n",
    "PlotPrediction(y,predict_cv(lgbm_models,X_train_clean_encoded),\"Train.csv: \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "square-construction",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictLGBM = lgbm_best.predict(X_test_clean_encoded)\n",
    "submissionLGBM = pd.DataFrame({'Id':test_Id,'SalePrice':predictLGBM})\n",
    "display(submissionLGBM.head())\n",
    "\n",
    "predictLGBM_CV = predict_cv(lgbm_models,X_test_clean_encoded)\n",
    "submissionLGBM_CV = pd.DataFrame({'Id':test_Id,'SalePrice':predictLGBM_CV})\n",
    "display(submissionLGBM_CV.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eastern-right",
   "metadata": {},
   "outputs": [],
   "source": [
    "##% Submit Predictions \n",
    "submissionLGBM.to_csv('submissionLGBM4.csv',index=False)\n",
    "submissionLGBM_CV.to_csv('submissionLGBM_CV4.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "emerging-directive",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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